A Brain-Controlled Interface Based on Augmented Reality for Evolutionary Artificial Intelligence

Abstract

From the medical sector to agriculture, from energy to transport, In every industry Adopting artificial intelligence (AI) is undergoing a revolution, There is a lot to do in AI in the future. This paper illustrates the concept of a brain-controlled interface for progressist artificial intelligence. brain computer interference in the living mind and actuators have shown great ability as a real-time bidirectional link. Artificial intelligence, which may lead to explication and decoding of nerve activity turbocharging the area of BCIS (BCIS) are systems that empower the user to control peripherals with brain signals. Bci can take advantage of AR techniques to explore new ways of interacting with the corporeal and factual world and displaying feedback. It is important that users can see and control their brain activity or size. This device can help severely disabled people who are paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral induration, brain stem stroke, or funiculus injury.

Introduction

I. INTRODUCTION

A brain computer interface also knows as brain machine interface,it is communicated between the brain's electrical activity and an external device,it's commonley computer or robotics limb.BCIS are commonly operated at researching ,mapping ,assisting, augmenting and to warm  human perceptional or sensory motor function.Execution of  the bcis is no offense(EEG,MEG,EOG,MRI) or partly aggressive(ECOG and endovascular) to aggresive(microelectrode array) based on how close electrodes find to brain tissue. With the huge explosion of technology, the frontier among humans and machines has started to be parochial. The description of "mind control" in our fantastic science fiction slowly with the help of machines. The edge of the new methods is the brain-computer interface(BCIS) and artificial intelligence(AI). The new model for BCIS  and AI was usually growing and applied not dependent on each other. Now scientists like to mix BCIS, and AI, thereby efficiently using the brain's electrical signals in external devices. A BCI could be a Artificial intelligence system which can perceive a convinced set of arrangement inbrain signals following five successive stages: signal purchase, preprocessing orsignal improvement, feature eradication, allotment and the control interface. The signal purchase stage apprehension the brain signals and may also perform noise devaluation and antiquity processing. The  pro-cessing stage arrange the signals in a proper form for more processing. The detail elimination stage classify discriminative instruction in the brain signals that have been listed. Once systematic, the signal is mapped onto a vector containing efficient and discriminant features from the observed signals. The extraction of this  attractive information is a very challenging task.Brain signals are varied with other signals coming from a finite agreed of brain activities that overlap in the pair time and space. also the signal is not usually static and may also be wry by relic such as electromyography (EMG) or electrooculography (EOG). The feature vector must also be of low importance, in order to reduce feature eradication stage complexity, but without proper information loss. The analysis stage allocate the signals taking the feature vectors into account. The choice of good particular features is therefore crucial to achieve impressive pattern recognition, in order to analyze the user’s intentions. certainly the control intersection stage translates the confidential signals into substantial direction for any connected device, such as a wheelchair or a computer. In the present situation use of BCIS in AR applications are comparatively small. To date, most of the AR-BCI literature has fastened on a spacious limit of electroencephalography(EEG)-based called possibility used to fifields, namely robotics, medicine, home automation navigation, or neurofeedback.Gravely ,some of this research have evaluated the effects of AR response on spiritual workload or tendency weighed to a consecutive feedback form.For example, chin et AI.(2010) weighed 3D-AR displays vs consecutive 2D reactions (Both shown on a computer screen) or construct that in the face of the upper spiritual weight adapted by the competition during the 3D-AR assessment, competition indicated the 3D-AR assessment being more fascinating or impulsive.

II. APPLICATIONS OF BCIS BASED ON AI

A. Applications In Cursor Control

New studies focused on the control of special computer mouse cursors for paralyzed patients through BCIs with high usefulness. The major components of a cursor control BCI comprise a sensor to record neural signals, a editor to explain  operation  intentions, and a computer cursor that interacts with the extraneous environment. On the basis of scalp EEG, ECOG and coincident memorized hidden , many BCI systems for cursor control have been developed, such as the P300 matrix manual  and the accelerated serial visual appearance method. The Brain Gate cluster conducted the primary human trial of a motor BCI beginning in Gregorian calendar month 2004, that record signals from a Blackrock 96-channel MEA  confined  in the arm area of M1 in a toleran with tetraplegia subsequent cervical spinal cord injury. They perfect two-dimensional evolution of a cursor on a screen and afterwards  used this “neural cursor” to direct the movement of a robotic limb.

B. Applications In Neuroprosthetics And Limb Rehabilitation

The work perplexity of the BCI recitation has briskly progressive over a jurisdiction of 2d and 3d.The cursor to restraint of more essential  demeanor on the computer screen Reaching and grasping, self-concealing.This fast-growing technology works by repairing an array of technologies, using a controlled robotic arm of tea.Electrodes are recorded either in the motor cortex of the individual involved in the area of the brain and carrying out activities. Brain activity is recorded when a person engages in cognitive functions, imagining that they are moving their hands, and used to direct a robotic organ.Many beneficial  action have been developed to help stroke patients achieve some function in the affected organ. However, about 80% of the strokes left with the upper limb motor losses do not asset from these methods. As a holistic theory the closing of the loop between the cortex of motor intention and activity which produces the reactionary reaction activity can restore the power of functional corticospinal and corticocular connection. Further, studies have shown that the use of the BCI can take effect in chronic stroke motor rehabilitation and have demonstrated that stroke patients, for restructuring their cortex and the rest of the cortex with functional and structural affiliation. To learn about neuroprosthetics control large - scale modifications of the cortical network have been activated.

C. Applications In Somatosensation

The movement to treat the patients with paralysis is highly dependent on somatosensory reactions, particularly hermaphrodite and tactile response. Cynoreceptor receptor signals in our skin give information about space.Contacts as well as the forces on the skin when we understand something the receptor lapis largely eliminate its ability to plan the movement of organ movements. In view of the importance of introspect, development  the didirectional bcis is essential.The activity of the sensors on artificial sensation can be transferred to activate neurons with these receptive areas. We can find the link between the pressure exerted by the prosthesis and objects.Hence, with the help of India, the appropriate intraartical microstimulation (icms) pulses are considered to be more efficient and efficient non - linear classifiers of bcis random one feasible and convenient nonlinear classifiers, and somatosanitary has carried out extensively using the fukunga - kuntas transform-based feature extraction method to promote capacity based bsis, thereby improving efficiency from 70% to 75%.

III. FEATURES

BCI devices have 4 components which are given below;

A. Signal Acquisition

Brain signals are dependable by signal purchase using proper sensors. These signals are  coincidental and may processed to remove distortion like notice and it level up to its applicable electronic processing. After all these this signals are digitalized and transferred to another computer.

B. Feature Extraction

Feature eradication is the process to accept the characteristics of signal and present it to the appropriate form for translation of command.

C. Feature Translation

After the feature eradication the amplified or processed signals are passed through feature translation algorithm after that it change the signal into applicable form. The features translation algorithm must be dynamic because it can make up any environment.

D. Device Output

The output command which is come from the feature translation will operate the out device or external devices.

Brain Computer Interface has so many advantages,

1. It helps physically disorder or Paralysed people to control artificial limbs with their mind.
2. BCI devices help gamers’ to control games with their mind.
3. It sends data to the mind of a deaf person this way they can hear any external sound.
4.  It also sends visual images to the mind of a blind person and they can see.

BCI has drawbacks or disadvantages also;

a. When electrodes are placed inside the skull which creates scar tissue in the brain.

b. Research of the BCI are not enough, right now it is in the starting stages .

c. Present BCI technologies are unfiltered.

IV. LITERATURE SURVEY

A. In The Paper 1: Brain Computer Interface Based Meal Assist Robot Control System

In 06 March 2020,this paper was published by Jihyeon Ha ,Laehyum kim.The brain computer interface (BCI)analysis  has developed adequate to administer for everyday use. Support Equipment for the assistance of the paralyzed and older persons have been developed.There are a few meal-assist robots previously on the market. They matured BCI- Based meal - assisted robot systems in which the start of a meal is brought about by an eye blink, and the choice of the food is positioned on a stagnant arena. The capacity and next meal are brought about by an electromyogram. In daily life older adults or people with ailment face challenges in their daily life without a doctor who helps them in their daily activities such as eating and walking. This system is very helpful for them.

B. In The Paper 2: Artificial Intelligence In Brain Computer Interface

In 09 june 2022 ,this paper was published by Abdulhamit subasi in the international congress on human computer interaction,optimization and robotic applications.The brain computer interface (BSI) is a link between the brain and foreign apparatus. Motor imagery (mi) has been approved as an adaptable cognitive technique for enhancing motor skills and for progress disorder reclamation analysis. Techniques of artificial intelligence (AI) play a crucial role in attentive changes to the brain signal and transforming them into allot of control signals. Classifiers are used to detect signals from the brain and control laties in real-time. The movement identical with AI can be ably confidential. This article USES a wave packet fragmentation feature extraction approach to improve misidentify accuracy. The suggested access has classified the related brain signals using the mixing techniques. Also, The approach KNN ensemble with the proposed adaboost may also perform more for the mi classification of classification accuracy 94.57% for the subject independent case.

C. In The Paper 3: Experiencing Bci Control In A Popular Computer Gane

In 21 march 2013 ,this paper was published by Bram van de Laar, Hayrettin Gürkök, Danny Plass-Oude Bos, Mannes Poel, and Anton Nijholt .The brain-computer interface is being developed not only to help the disabled with motor replacement, motor recovery, and new communication prospects but also as an instrument for healthy users of entertainment and gaming. A BCI control channel based on parietal alpha band power is used to control the avatar size and function in the game. The participants themselves can indicate when they want to stop playing. The actual and approximate period was recorded and the questionnaire was applied to the operation and control of the questionnaire. When using a BCI, the control and participation were short-sighted. But the BCI control did not lessen the fun. During the interview, experienced players said they saw potential in the basis's application in a complex interface game like wow. This study shows that bci as a supplementary control can be used as an entertaining and natural keyboard/mouse control even though the amount of control is limited.

Conclusion

A brain-computer interface, also known as a brain-machine interface, communicates between the brain\'s electrical activity and an external device, its common computer or robotics limb. BCIS is commonly used in researching, mapping, assisting, augmenting, and warming human perceptional or sensory-motor function. The execution of the basis is no offense (EEG, MEG, EOG, MRI) or partly aggressive (ECOG and endovascular) to aggressive (microelectrode array) based on how close electrodes find to brain tissue. With the huge explosion of technology, the frontier between humans and machines has started to be parochial. The description of \"mind control\" in our fantastic science fiction slowly with the help of machines. The edge of the new methods is the brain-computer interface (BCIS) and artificial intelligence(AI). The new model for BCIS and AI was usually growing and applied not dependent on each other. Now scientists like to mix BCIS, and AI, thereby efficiently using the brain\'s electrical signals in external devices.

References

[1] Abdulhamit Subasi, Artificial Intelligence in brain computer interface, ICHCI,June 2022. [2] A. D. Bigirimana , N. Siddique, D. Coyle, Emotion inducing Imagery versus Motor Imagery for a brain-computer interface, IEEE Transactions on neural systems and rehabilitation engineering, March 2020. [3] Seonghun Park, Jisoo Ha, Ho-Seung Cha, Kyeong-Gu Lee, and Chang-Hwan Im, Development of an home appliance control system for the elderly based on ssvep-based brain computer interface, international winter conference on brain-computer interface, February 2021. [4] Jihyeon Ha ,Laehyum kim, A brain computer interface based meal assist robot control system, IEEE Transactions on neural systeams and rehabilitation engineering, March 2020. [5] Fiachra Matthews, Barak A. Pearlmutter, Tomas E. Wards, Christopher Soraghan, Charles Markham, Hemodynamics for Brain-Computer Interfaces, January 2008. [6] https://www.researchgate.net/figure/Components-of-a-BCI-system-signals-from-the-users-brain-are-acquired-and-processed-to_fig1_267792090. [7] https://link.springer.com/article/10.1007/s10514-017-9622-4. [8] https://www.researchgate.net/figure/The-block-diagram-of-a-BCI-system_fig1_221971719.

Copyright

Copyright © 2022 Dipankar Pariary, Koushik Pal, Karan Kumar, Arindam Roy, Rupayan Dirghangi, Sujoy Dutta, Rahul Bera, Manosijo Ganguly. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.